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Condition: Ischemic Stroke
Education: Learning
Procedure: MRI Scan

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Total 42 results found since Jan 2013.

Predicting Future Brain Tissue Loss From White Matter Connectivity Disruption in Ischemic Stroke Clinical Sciences
Conclusions— ChaCo scores varied, but the most affected regions included those with sensorimotor, perception, learning, and memory functions. Correlations between baseline ChaCo and subsequent tissue loss suggest that the Network Modification Tool could be used to identify regions most susceptible to remote degeneration from acute infarcts.
Source: Stroke - February 24, 2014 Category: Neurology Authors: Kuceyeski, A., Kamel, H., Navi, B. B., Raj, A., Iadecola, C. Tags: Computerized tomography and Magnetic Resonance Imaging, Pathology of Stroke Clinical Sciences Source Type: research

Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke Clinical Sciences
Background and Purpose—Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We applied the novel extreme gradient boosting algorithm for multimodal magnetic resonance imaging–based infarct prediction.Methods—In a retrospective analysis of 195 patients with acute ischemic stroke, fluid-attenuated inversion recovery, diffusion-weighted imaging, and 10 perfusion parameters were derived from...
Source: Stroke - March 26, 2018 Category: Neurology Authors: Michelle Livne, Jens K. Boldsen, Irene K. Mikkelsen, Jochen B. Fiebach, Jan Sobesky, Kim Mouridsen Tags: Magnetic Resonance Imaging (MRI), Ischemic Stroke Original Contributions Source Type: research

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning Clinical Sciences
Background and Purpose—Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume.Methods—Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNNdeep) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with...
Source: Stroke - May 25, 2018 Category: Neurology Authors: Anne Nielsen, Mikkel Bo Hansen, Anna Tietze, Kim Mouridsen Tags: Magnetic Resonance Imaging (MRI), Treatment, Quality and Outcomes, Statements and Guidelines, Ischemic Stroke Original Contributions Source Type: research

Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke
AbstractHyperglycemia is a feature of worse brain injury after acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with hyperglycemia and a neuroimaging proxy for cytotoxic brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an acute stroke. Glucose was measured by a central clinical laboratory, and a s...
Source: Translational Stroke Research - October 17, 2020 Category: Neurology Source Type: research

Shengui Sansheng San Ameliorates Cerebral Energy Deficiency via Citrate Cycle After Ischemic Stroke
Conclusion In summary, SSS extraction significantly ameliorates cerebral energy metabolism via boosting citrate cycle, which mainly embodies the enhancements of blood glucose concentration, glucose and lactate transportation and glucose utilization, as well as the regulations of relative enzymes activities in citrate cycle. These ameliorations ultimately resulted in numerous ATP yield after stroke, which improved neurological function and infarcted volume. Collectively, it suggests that SSS extraction has exerted advantageous effect in the treatment of cerebral ischemia. Ethics Statement All animal operations were accor...
Source: Frontiers in Pharmacology - April 22, 2019 Category: Drugs & Pharmacology Source Type: research

What Are the Classifications of Perinatal Stroke?
Discussion Perinatal stroke occurs in about 1:1000 live births and is a “focal vascular injury from the fetal period to 28 days postnatal age.” Perinatal stroke is the most common cause of hemiparetic cerebral palsy and causes other significant morbidity including cognitive deficits, learning disabilities, motor problems, sensory problems including visual and hearing disorders, epilepsy, and behavioral and psychological problems. Family members are also affected because of the potential anxiety and guilt feelings that having a child with a stroke presents, along with the care that may be needed over the child&#...
Source: PediatricEducation.org - May 1, 2023 Category: Pediatrics Authors: Pediatric Education Tags: Uncategorized Source Type: news

Machine learning identifies stroke features between species
Conclusion: Our findings reveal that the segmentation produced by our proposed framework using 24 h MRI rat data strongly correlated with the final stroke volume, denoting a predictive effect. In addition, we show for the first time that the stroke imaging features can be directly translated between species, allowing identification of acute stroke in humans using the model trained on animal data. This discovery reduces the gap between the clinical and preclinical fields, unveiling a novel approach to directly co-analyze clinical and preclinical data. Such methods can provide further biological insights into human stroke an...
Source: Theranostics - January 15, 2021 Category: Molecular Biology Authors: Salvador Castaneda-Vega, Prateek Katiyar, Francesca Russo, Kristin Patzwaldt, Luisa Schnabel, Sarah Mathes, Johann-Martin Hempel, Ursula Kohlhofer, Irene Gonzalez-Menendez, Leticia Quintanilla-Martinez, Ulf Ziemann, Christian la Fougere, Ulrike Ernemann, Tags: Research Paper Source Type: research

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
Source: Frontiers in Neurology - August 22, 2022 Category: Neurology Source Type: research

Pregnancy-Related Stroke: A Review
Conclusions and Relevance Early recognition and management are integral in decreasing the morbidity and mortality associated with a stroke in pregnancy. Relevance Statement This study was an evidence-based review of stroke in pregnancy and how to diagnose and mange a pregnancy complicated by a stroke. Target Audience Obstetricians and gynecologist, family physicians Learning Objectives After completing this learning activity, the participant should be better able to identify the pregnancy-related risk factors for a stroke; explain the presenting signs and symptoms of a stroke in pregnancy; describe...
Source: Obstetrical and Gynecological Survey - June 1, 2022 Category: OBGYN Tags: CME ARTICLES Source Type: research

Cognitive state following mild stroke: A matter of hippocampal mean diffusivity
This article is protected by copyright. All rights reserved.
Source: Hippocampus - July 27, 2015 Category: Neurology Authors: Efrat Kliper, Einor Ben Assayag, Amos D. Korczyn, Eitan Auriel, Ludmila Shopin, Hen Hallevi, Shani Shenhar‐Tsarfaty, Anat Mike, Moran Artzi, Ilana Klovatch, Natan M. Bornstein, Dafna Ben Bashat Tags: Research Article Source Type: research

Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke
AbstractNowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on ...
Source: Medical and Biological Engineering and Computing - August 2, 2022 Category: Biomedical Engineering Source Type: research

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review
Biomed Res Int. 2022 Nov 14;2022:2456550. doi: 10.1155/2022/2456550. eCollection 2022.ABSTRACTIschemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for i...
Source: Biomed Res - November 24, 2022 Category: Research Authors: Liyuan Cui Zhiyuan Fan Yingjian Yang Rui Liu Dajiang Wang Yingying Feng Jiahui Lu Yifeng Fan Source Type: research

Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics
ConclusionThe ML models based on muti-modal MRI radiomics are of high value for predicting clinical outcomes in acute stroke patients.
Source: Frontiers in Psychiatry - January 9, 2023 Category: Psychiatry Source Type: research

Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence
Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in particular, deep learning, to synthesize large amounts of data and to understand better how clinical and imaging data can be leveraged to improve stroke care promises a new era of stroke care. In this article, we review common deep learning model structures for stroke imaging, evaluation metrics for model performance, and studies that investigated deep learning application in acute ischemic stroke care and secondary prevention.
Source: Topics in Magnetic Resonance Imaging - August 1, 2021 Category: Radiology Tags: Review Articles Source Type: research